Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

In vivo biopanning: a methodological approach to identifying novel targeting ligands for delivery of biological agents to the vasculature

In order for gene delivery to be clinically acceptable, a number of crucial developments need to be made to existing vectors. Significant advances have been made in the identification of novel platform vectors that possess modified tropism to the native vector, directing infectivity away from nontarget tissues such as the liver. In order to fully optimize these detargeted platform vectors, they need to be retargeted toward a chosen, defined site, which will be defined according to the disease studied. The successful transition of targeting peptides identified using in vitro screening protocols to an in vivo disease model may be compromized by the complexity of delivery into an intact biological system. To this end, peptides identified using in vivo biopanning may prove to be of greater clinical significance given that they were identified in the disease model of choice and so should translate more successfully to the intact preclinical model. Exploitation of the heterogeneity of the vascular endothelium using such an approach will go a long way toward improving the efficiency and achieving site-specific gene expression, with important clinical implications for the systemic application of gene delivery

An introductory electrochemical approach to studying hydrometallurgical reactions

Many hydrometallurgical processes are electrochemical phenomena. Although this fact is well known, electrochemical methods are under-utilized when studying these processes. One possible reason is the confusion surrounding the number of electrochemical techniques available. We present an insight into how one technique can be used to study a variety of hydrometallurgical processes. This article details how to perform experiments, interpret data, and recognize complications with results. Examples where this approach has been highly successful are presented

A molecular classification of papillary renal cell carcinoma

10.1158/0008-5472.CAN-05-0533Cancer Research65135628-563